Allosteric regulation of proteins is often examined using two different models. The widely-known “induced-fit” (IF) model proposes that effectors form a loose complex with inactive proteins and cause them to shift into a new, active conformation. In the competing “conformational selection” model, effectors bind to and stabilize proteins that are already in an active conformation. An upcoming paper in the Journal of the American Chemical Society examines this question in the case of T. lanuginosis lipase (TLL) (1). The data show that the enzyme enters an activated state even when it is prevented from interacting with its activator. While this strongly suggests that the activation mechanism is CS, some data suggest that the mechanism is actually IF.

The paper in question relies on single-molecule kinetics techniques to characterize an enzyme. Previous studies in this field have shown that reaction time varies between enzyme molecules and over time for single molecules. These findings should not surprise us, knowing as we do that all machines have intrinsic variation in their rates of operation. Flexible proteins that can adopt many different folded structures (not to mention partially-folded and unfolded ones) should be expected to have even more operational differences. That said, there are a variety of ways to account for the observed distribution of reaction rates.

TLL is activated by lipid membranes. While tracking the activity of individual enzymes using fluorescence, Hatzakis et al. altered their ability to access a lipid membrane by changing the concentration of polyethylene glycol (PEG) in the solution; PEG blocks the (tethered) enzyme from accessing the liposome. They found that a model where the enzyme exists in an equilibrium of active (R) and inactive (T) states is most consistent with the distribution of reaction times they observe, even at PEG levels that completely occlude the membrane. Based on this finding, they conclude that TLL activation occurs by selection of an active conformation from a pre-existing equilibrium, rather than inducing a new conformation.

At this point things start to get a little confusing. The central problem is that CS and IF are used to identify both characteristics of the apo- ensemble and features of the activation pathway, and the former don’t necessarily coincide with the latter.

To understand what I mean, take a look at the figure below. Here, Ta and Ra are ligand-free T and R states, while Tb and Rb are ligand-bound T and R states. The typical ligand-free state is Ta, and the allosterically activated state is Rb. Ra (apo-R state) and TbL (“encounter complex”) are thermodynamic states that are viewed as characteristic of CS (red path) and IF (blue path), respectively. The rates kact and kin are the apparent rates of activation and inactivation, which are dependent on the microscopic rates noted for each pathway.

In a CS mechanism, the protein adopts both the R and T structures while free in solution, and ligand binds preferentially to the R state and stabilizes it, redistributing this pre-existing equilibrium without creating “new” states. Because binding follows conformational change, a pre-existing equilibrium in the apo- ensemble is a necessary condition of CS.

In the IF case, binding precedes conformational change: the ligand encounters the free T structure and allows it to adopt a “new” R structure. Traditionally, this has been interpreted to mean that the R structure never exists in solution at all. However, binding may proceed by an induced-fit mechanism even if an R state is populated in solution.

There are a couple of cases where we know this must happen. For instance, adenylate kinase, a protein that I have discussed before, undergoes conformational exchange between open (T) and closed (R) states in solution. However, in the closed state the ligand-binding site is completely occluded, and it is impossible for ligands to bind to this state. It therefore follows that binding-associated conformational change proceeds by an IF-like pathway, even though an equilibrium between the R and T structures exists in the apo- state. In this and similar cases, all four major states are populated, but kon,R≈0 and so the path through TbL dominates the reaction flux.

The thermodynamic implication of CS — that there is a detectable equilibrium between R and T states — is not synonymous with its mechanistic meaning — that conformational change precedes binding. This makes sense, because in the context of a constantly interconverting ensemble of conformations, even very unfavorable Ra states will be accessed occasionally. The strict thermodynamic definition of IF, that the R conformation be unattainable in the apo- state, may not apply to any real protein (2). However, the population of R conformers may be so low and short-lived as to be undetectable.

Even though a pre-existing equilibrium is not probative, a quick examination of the figure above indicates how we can distinguish between these mechanisms. In the case of CS, the rate of interconversion between T and R states in solution sets an upper limit on the activation rate, because the ligand binds to the apo-R state. At high ligand concentrations, kact = kTR because the presence of ligand probably will not alter the energy landscape of a protein it is not bound to. In this mechanism, however, koff,R is expected to be much slower than kRT. This implies that kin should decrease significantly at high ligand concentrations.

In an IF mechanism, the energy landscape of the encounter complex need not be the same as that of the apo- protein. As such, in IF activation the T→R energy barrier can (and is expected to) become lower. Accordingly, if kact exceeds kTR at high ligand concentrations (3), then an IF mechanism must be at work. Because this energy barrier is variable in an IF mechanism, however, it’s somewhat difficult to predict what will happen with the R→T barrier; it might get larger, or it might not. The figure below summarizes the expectations.

Hatzakis et al. report that the rate of conversion from T to R (i.e. kact) increases as PEG concentration decreases (note: in the advance online version the schemes in Figures 2 and 4 are mislabeled, but the energy diagram in 4 is accurate). The kin rate, by contrast, remains constant. If we accept their (reasonable) assumption that the energy of Ta is not affected by the lipid membrane, this indicates that the T→R energy barrier decreases in the presence of the allosteric effector. That, in turn, implies that the membrane is associated with the protein prior to the transition state, and thus that the mechanism of activation is induced fit, even though an Ra state can be observed in solution.

“Conformational selection” is often used interchangeably with “pre-existing equilibrium”, but it is dangerous to employ this equivalence. The thermodynamic feature of a pre-existing equilibrium between apo -inactive and -active states does not necessarily imply that the pathway between apo-inactive and bound-active states proceeds through an apo-active intermediate. In some cases, the observed equilibrium indicates a kinetic dead-end where kon,R≈0 and the reaction flux is dominated by IF mechanisms.

Hatzakis et al. studied the single-molecule kinetics of several other allosterically-regulated monomeric enzymes and found that they also showed evidence of a pre-existing equilibrium between active and inactive states. This alone, however, is not sufficient to establish activation via CS. Only a detailed examination of the kinetics can indicate whether activation uses CS, IF, or some combination of these mechanisms.

2) By the same token, apo-R states are almost certainly not exactly the same as bound-R states, so a strict version of CS is also quite improbable.

3) At substoichiometric concentrations of ligand, kact can exceed kTR because in this condition maximum activation can be reached by (rapid) binding of ligand to the existing pool of Ra, without any need to replenish Ra from Ta.

Fluorescent sensors, be they proteins or small molecules, are extremely useful because they can be used to detect metabolic states and protein interactions in living cells. Fluorescent proteins are particularly useful because they can be produced inside the cell and, using tags, targeted to specific proteins, locations and organelles quite easily. Because of this, a large number of fluorescent proteins have been isolated and engineered, usually using the backbone of the green fluorescent protein. In this week’s Proceedings of the National Academy of Sciences, a group from Columbia University describe a fluorescent protein that can sense the viscosity of its surroundings (1).

Crystal model of dark-state Dronpa (PDB: 2POX), chromophore in green

Kao et al. were working with a protein called Dronpa. Isolated from coral, Dronpa is structurally homologous to GFP, but has unique photoswitching characteristics (2). Irradiation of Dronpa at 488 nm causes fluorescent emission at 518 nm, but the fluorescence is rapidly quenched, and emission ceases.

Dronpa can be restored from this ‘dark state’ by hitting it with light at 405 nm, completely recovering its fluorescence. A pulse of light at 405 nm while also illuminating at 488 nm causes a rapid spike in fluorescence, followed by a slower decay over several milliseconds as the bright state quenches. Kao et al. decided to look further into Dronpa’s photoswitching behavior by examining the kinetics of this process.

When they examined the decay rates, they noticed something interesting: the rate of quenching seemed to depend on the viscosity of the solvent, which they controlled by varying the percentage of glycerol in their measurement buffer. Unfortunately, as you can see below, the distributions overlapped significantly, meaning that this protein would not be a particularly good viscosity sensor.

Modeled distributions for WT Dronpa at various glycerol concentrations

This prompted Kao et al. to ask if they could do better. The magnitude of the changes in rates that they saw suggested that viscosity-related changes in the photoswitching rate were responsible for the effect. This makes sense, because the structural models suggest that certain translational motions are needed to switch the chromophore from its light state to its dark state. These motions would be subject to drag from the surrounding liquid, which increases with viscosity.

An alternative possibility not discussed by Kao et al. is that the internal fluctuations of the protein are directly coupled to solvent dynamics. This coupling, called ‘solvent slaving’ (3), has long been suggested by Hans Frauenfelder based on his work in myoglobin.

In either case, if protein flexibility is the key to the photoswitching rate, then a more flexible protein might be more sensitive to viscosity. As it turns out, some flexible mutants of Dronpa already exist. Specifically, Dronpa-3 has been engineered to have both a steric clash and a void, and has a reduced quantum yield that is consistent with a more flexible interior.

When Kao et al. repeated their viscosity experiments using Dronpa-3, they found that it had substantially better measurement characteristics. The decay rates still had fairly broad distributions, but were much better separated (see below). Also, consistent with their hypothesis that protein flexibility contributed significantly to the switching rate, the decays were much faster overall (compare the scales). The response is not linear over the entire viscosity range, but it still seems that Dronpa-3 could produce relatively sensitive measurements.

In order to test that idea, the authors expressed Dronpa-3 alone, and as a fusion with a histone protein, in HEK 293T cells. In these experiments, the researchers were able to measure local viscosity in live cells, both during stable phases and mitosis. The results suggested, not surprisingly, that the nucleus is more viscous than the cytoplasm, though the environment seemed to be more heterogeneous once chromatin had condensed for mitosis. All this is more or less as expected, and in some cases cross-validated by other experiments. In the future, Dronpa-3 may be useful for examining solution dynamics during other processes that reshape cells and tissue or depend on molecular diffusion, as well as in calibrating in vitro experiments to better reflect the relevant biological environments.

In previous posts on this blog I’ve discussed efforts to perform NMR inside of living cells. These experiments, performed in bacteria, are primarily intended to establish whether dilute-solution experiments veridically reproduce biomolecular structures as they appear in live organisms. Now it seems that crystallography is starting to get in on the act. This week in Nature Methods, a German-American collaborative team report X-ray diffraction patterns from protein crystals grown inside cultured insect cells (1).

This is not the first time such crystals have been observed. Typically, they are associated with the expression of a protein called polyhedrin that is part of a baculovirus – the vehicle used to insert foreign DNA into these cells. Other proteins will also form crystals when fused to polyhedrin itself or parts of its DNA. However, X-ray diffraction patterns had not previously been obtained from these crystals.

The reason for this is one of scale. As you might imagine, crystals grown in living cells cannot be much larger than the dimensions of the cells themselves. Such tiny crystals will not yield usable diffraction data using ordinary techniques. Even if some diffraction data can be squeezed out of them, the crystals will be destroyed by the radiation before a full dataset can be collected.

Koopmann et al. get around this problem using serial femtosecond X-ray crystallography (SFX), a technique in which a powerful X-ray laser is focused on the tiny crystals. The super-intense beam rapidly vaporizes the protein crystal, but that intensity also produces a little diffraction data. By firing the laser at a stream of these nanocrystals, in principle a full diffraction pattern can be collected and used to determine a structure.

The authors of this study gathered the crystals by gently lysing the cells and separating the detritus with a centrifuge. Notably, this allows them to skip much of the tedious business of protein purification, which may be enough reason to use this technique even if the crystals themselves have no practical application. The authors then subjected some of the nanocrystals to SFX, gathering some diffraction data. Due to limits in dataset size, the authors were unable to solve the structure using this approach (the paper reports a structure from recrystallized protein), but the results seem to demonstrate the eventual feasibility of such a project.

Of course, if one has doubts about the biological relevance of a crystal structure, those doubts are unlikely to be assuaged just by the fact that a crystal grew inside a cell, as this is an abnormal event. There may be different benefits to this technique, however. The protein used here was a glycosylated enzyme from Trypanosoma brucei, a unicellular pathogen that causes sleeping sickness. This class of proteins can pose a special challenge for structural biology because glycosylation can interfere with crystallization, and is not readily reproduced during protein expression in bacteria. The approach of allowing crystals to form within insect cells that can accurately replicate the relevant glycosylation patterns may provide a significant advantage in attacking this type of structural problem. The approach could conceivably have similar advantages for membrane-associated proteins. This may, in turn, open up new targets for drug design.

Imagine that you could get an injection of a protein that would chop up arterial plaques. Imagine that you could drop a plastic bottle into a pool of bacteria that would transform it back into high-grade oil. Imagine that you could take any organic material at all and, with a minimum of planning, transform it into any kind of desired organic chemical with a bare minimum of energy input and no need to purify intermediates. This is the vision behind the applied structural biology of protein design, the holy grail of which is to come up with a way to make enzymes that will perform novel chemistry. A study recently published online in Nature Biotechnology by David Baker’s group (1) suggests that the design process could be improved by crowdsourcing certain parts of the problem to gamers (the paper is paywalled at Nature but freely available via the Foldit site).

An enzyme is a protein that increases the rate of (catalyzes) a chemical reaction, often by incredible amounts. The best enzymes can increase reaction rates by factors of up to 1017 relative to the same reaction occurring in pure water. Protein design aims to produce artificial enzymes with rate enhancements comparable to their natural counterparts. To do this, biochemists try to design an active site that stabilizes the transition state of a chemical reaction. The transition state is the point of a reaction where the molecules are in their least stable state, and equally likely to revert to substrates or continue on and become products.

Unfortunately, it’s not just as simple as stabilizing a transition state. Enzymes have to bind and release their substrates and products, producing energy landscapes that are at least as complex as the one I have drawn below. Using a protein design protocol they had described in previous publications, Baker’s group managed to produce a weak enzyme. They then asked the Foldit players to help out, by posing some specific challenges to try and stabilize the bound substrates. The Foldit players eventually produced an 18-fold improvement in the enzyme’s kcat/KM value. To understand what that means and what the players accomplished, let’s examine this reaction coordinate:

That’s a busy little figure, but it’s not as bad as it looks. The position up or down in the figure indicates how much energy a state has. The more energy, the less likely the system is to occupy that state. Left to right positions show us how close we are to the desired state of the system, which is to have the product (P) we want separate from the enzyme (E) that catalyzed its production from substrate (S). To move from one stable state to another stable state, you have to push the system over hills (energy barriers) in the landscape, just like pushing a car up a hill. The higher the barrier, the slower that step becomes. For simplicity, this diagram shows only one substrate, but the artificial enzyme had two. We can pretend that the Foldit effort started with an enzyme that resembled the blue curve.

We start with E and S separate from each other in solution (E+S). E and S bind to each other to form ES, releasing binding energy. Here I’ve shown a small barrier between E+S and ES, but in many cases there is no barrier here, or it is negligible. Next S is converted to P, and as you can see there is usually a large energy barrier, at the top of which is the transition state (TS). The height of the barrier is determined by the activation energy, which is affected by the structure of the enzyme-substrate complex. Once P has been formed, the complex dissociates so we have free enzyme and product (E+P). Here I have shown E+P to be a lower-energy state than EP, but this won’t necessarily be true.

In the language of Michaelis-Menten kinetics, this landscape is described by two main parameters. KM, also called the Michaelis constant, describes the balance between E+S and ES, and therefore primarily reflects the binding energy. The larger the binding energy, the more ES will be favored, and the lower KM will be. The turnover number, or kcat (maybe we should call this the Menten constant?) describes the creation of product over time, and in this diagram it depends on the activation energy. Again, the larger the activation energy, the lower kcat will be. However, kcat really just depends on the slowest step of the catalytic cycle. If the largest energy barrier was between EP and E+P, kcat would depend on that barrier. Because kcat/KM is something like a normal rate constant, and combines the values in an easy-to-understand way (a higher kcat/KM means a better enzyme), it’s often used to describe an enzyme’s activity.

So how did the Foldit players improve the activity by a factor of 18? The original enzyme design left part of the active site open to water. Through a series of iterations, the Foldit players filled in this void with a self-stabilizing helix-loop-helix motif (Figure 1b). The upshot of this was that the affinity of the enzyme for both substrates increased. Thus, KM decreased, as shown in Table 1, for both substrates. At the end of the process, the diene bound six times as tightly and the affinity for the dienophile improved by about a factor of three. This accounts for all the observed change in kcat/KM, because kcat was not improved.

Although it may not seem like it, we can also learn a great deal from the fact that kcat did not change. This observation shows that the changes made by the Foldit players did stabilize the TS. Otherwise, the energy barrier would have increased when they stabilized the ES complex. However, the best-case scenario would have been for them to uniquely stabilize TS without improving the energy of ES, because this would effectively lower the energy barrier and increase the reaction rate. Because this didn’t happen, the situation follows the orange curve in the figure above: the ES and TS states have shifted down in energy by the same amount, with no change to the activation energy.

The lack of change in kcat also indicates that the Diels-Alder reaction itself, rather than product dissociation, is rate-limiting for the enzyme. My reasoning here is that the increase in affinity is general. We know that both the ES and TS complexes were stabilized by the changes, so EP probably was too, as shown in the orange curve. If the EP → E+P transition were rate-limiting, these stabilizing mutations would have made the enzyme slower.

The Foldit players made this a better enzyme, but that doesn’t exactly mean that it’s an impressive one. The observed kcat is significantly slower than almost any natural enzyme, and the overall rate enhancement is on the order of 103-104, which is not much better than catalytic antibodies. The success of the Foldit players at improving the affinity of the enzyme for all the bound states suggests that it might be possible to use crowdsourced systems like Foldit to accomplish the more difficult feat of stabilizing a TS, or at least to generate folds that support a pre-defined TS. The ultimate goal is to produce something like the green curve, where substrate binding is stronger and activation energy is lower. I hope that such efforts will be taking place among the Foldit players soon, if they haven’t started already.

Disclaimer: I am part of an ongoing collaboration with David Baker’s group unrelated to the Foldit program.